However, we were recently stabbed by an assumption. I had to spend over 40 hours restructuring our workbook library and application code.

Go With What You Know

The point of an Agile approach is to build high-value things first. In the olden days, we would have spent months (really) writing a sophisticated set of hypothetical use cases for the workbook library and then designing something that would cover all possible bases.

Rather than spend endless hours on the potential workbook features, I wrote what we needed to read the workbook files we actually had. We had a mixture of XLS, XLS in ZIP files, and CSV files. So we unified those with a fairly simple model of a "Row Source" that provided information on sheets, and provided the sequence of rows.

However, all those spreadsheets had a common feature. They were built by people with a strong IT background, people who -- even if they couldn't define "Normalization" -- knew what normalized data looked like. They provided everything as proper columns.

Recently, we got some data for a new customer pilot that was just enough different that it was a costly problem.

What Changed?

The change was the use of the sheet tab names to carry meaningful key information.

Every previous example either had sheets with names like "sheet1", "sheet2" and "sheet3", or the sheet name was something we could filter on.

This workbook had the time dimension coded in the sheet names, not a column of data on each sheet. Suddenly, the worksheet name was significant. And that's not all.

How Bad Can It Be?

The extensive breakage came from a bad design decision buried in the workbook library and all application layers that depend on the workbook libraries. Assuming that data was in columns -- instead of sheet names -- didn't create a big problem. Unwinding that assumption was an easy to fix.

What was bad was a design that permitted the various mappings to be independent of each other. The "operation" classes that stepped through rows were designed so that a simple list of independent mappings could be used to extract relevant columns from a row and process them. Each independent mapping created a Python object from columns.

It turns out that each mapping needed a context (with worksheet name). Also, it turns out that some mappings actually depend on other mappings.

When the mappings are picking up columns, having several mappings depending on a single column is easy. Having several mappings depend on the context, wasn't too bad. Having one mapping that parsed the sheet name, exposed our wishful thinking.

We needed to have mappings that depended on each other. When we map the sheet name to a Python object, we did parsing and database lookups. Other mappings now must be "aware" of this mapping so they don't redo the parsing and database lookups.

Lessons Learned

The trivial (and wrong) lesson learned could be "don't make so many assumptions". That's silly. We didn't casually make assumptions. We had example data; the sample data was biased and didn't show all conceivable permutations.

Another trivial (and wrong) lesson could be "document all your assumptions". That's silly, too. We did document them. That doesn't make the breakage significantly easier to fix.

The real lesson is to avoid wishful thinking . We'd tried too hard to make all of the mappings into independent objects. The phrase "shared nothing" is our mantra. While shared nothing gave us a very composable design, it wasn't actually correct.

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About Me

Steven F. Lott is a consultant, teacher, author and software developer with over 35 years of experience building software of every kind, from specialized control systems for military hardware to large data warehouses to web service API's.